
"""simple docstring"""



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


a       :						int       		=		logging.getLogger(__name__)




class 		a_    :







    def __init__(      self      :							int  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = False







    def   _snake_case       (      self      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							str  )		->Tuple:






        '''simple docstring'''






        if not self.initialized:
            _UpperCAmelCase        = RagRetriever(
                __UpperCamelCase  ,       question_encoder_tokenizer=__UpperCamelCase  ,       generator_tokenizer=__UpperCamelCase  ,       index=__UpperCamelCase  ,       init_retrieval=__UpperCamelCase  ,       )
            _UpperCAmelCase        = True







    def   _snake_case       (      self      :							Optional[Any]  )		->int:






        '''simple docstring'''






        self.retriever.index.init_index()







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Union[str, Any]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.retriever._main_retrieve(__UpperCamelCase  ,       __UpperCamelCase  )
        return doc_ids, retrieved_doc_embeds







class 		a_    (			_UpperCAmelCase						):







    def __init__(      self      :							Any  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple=None  )		->int:






        '''simple docstring'''






        if index is not None and index.is_initialized() and len(__UpperCamelCase  ) > 0:
            raise ValueError(
                """When using Ray for distributed fine-tuning, """
                """you'll need to provide the paths instead, """
                """as the dataset and the index are loaded """
                """separately. More info in examples/rag/use_own_knowledge_dataset.py """  )
        super().__init__(
            __UpperCamelCase  ,       question_encoder_tokenizer=__UpperCamelCase  ,       generator_tokenizer=__UpperCamelCase  ,       index=__UpperCamelCase  ,       init_retrieval=__UpperCamelCase  ,       )
        _UpperCAmelCase        = retrieval_workers
        if len(self.retrieval_workers  ) > 0:
            ray.get(
                [
                    worker.create_rag_retriever.remote(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )
                    for worker in self.retrieval_workers
                ]  )







    def   _snake_case       (      self      :							Optional[int]  )		->List[str]:






        '''simple docstring'''






        logger.info("""initializing retrieval"""  )

        if len(self.retrieval_workers  ) > 0:
            ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]  )
        else:
            # Non-distributed training. Load index into this same process.
            self.index.init_index()







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Optional[Any]  )		->Optional[Any]:






        '''simple docstring'''






        if len(self.retrieval_workers  ) > 0:
            # Select a random retrieval actor.
            _UpperCAmelCase        = self.retrieval_workers[random.randint(0  ,       len(self.retrieval_workers  ) - 1  )]
            _UpperCAmelCase     ,_UpperCAmelCase        = ray.get(random_worker.retrieve.remote(__UpperCamelCase  ,       __UpperCamelCase  )  )
        else:
            _UpperCAmelCase     ,_UpperCAmelCase        = self._main_retrieve(__UpperCamelCase  ,       __UpperCamelCase  )
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Tuple  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							str=None  ,       **__UpperCamelCase      :							Any  )		->Union[str, Any]:






        '''simple docstring'''






        return super(__UpperCamelCase  ,       cls  ).get_tokenizers(__UpperCamelCase  ,       __UpperCamelCase  ,       **__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Union[str, Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							str  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = kwargs.pop("""config"""  ,       __UpperCamelCase  ) or RagConfig.from_pretrained(__UpperCamelCase  ,       **__UpperCamelCase  )
        _UpperCAmelCase        = RagTokenizer.from_pretrained(__UpperCamelCase  ,       config=__UpperCamelCase  )
        _UpperCAmelCase        = rag_tokenizer.question_encoder
        _UpperCAmelCase        = rag_tokenizer.generator
        if indexed_dataset is not None:
            _UpperCAmelCase        = """custom"""
            _UpperCAmelCase        = CustomHFIndex(config.retrieval_vector_size  ,       __UpperCamelCase  )
        else:
            _UpperCAmelCase        = cls._build_index(__UpperCamelCase  )
        return cls(
            __UpperCamelCase  ,       question_encoder_tokenizer=__UpperCamelCase  ,       generator_tokenizer=__UpperCamelCase  ,       retrieval_workers=__UpperCamelCase  ,       index=__UpperCamelCase  ,       )

"""simple docstring"""



import warnings
from functools import wraps
from typing import Callable


def 			_UpperCamelCase						(    _A   )     ->	Callable:





    """simple docstring"""

    @wraps(_A   )
    def _inner_fn(*_A       ,   **_A   ):
        warnings.warn(
            (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")       ,   _A       ,   )
        return fn(*_A       ,   **_A   )

    return _inner_fn